Erik Bülow1,2, Ute Hahn3,4, Ina Trolle Andersen3, Ola Rolfson1,2, Alma B Pedersen3,5, Nils P Hailer6. 1. The Swedish Arthroplasty Register, Centre of Registers Västra Götaland, Gothenburg, Sweden. 2. Department of Orthopaedics, Institute of Clinical Sciences, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 3. Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark. 4. Department of Mathematics, Aarhus University, Aarhus, Denmark. 5. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark. 6. Department of Surgical Sciences/Orthopaedics, Uppsala University Hospital, Uppsala, Sweden.
Abstract
Purpose: To develop a parsimonious risk prediction model for periprosthetic joint infection (PJI) within 90 days after total hip arthroplasty (THA). Patients and Methods: We used logistic LASSO regression with bootstrap ranking to develop a risk prediction model for PJI within 90 days based on a Swedish cohort of 88,830 patients with elective THA 2008-2015. The model was externally validated on a Danish cohort with 18,854 patients. Results: Incidence of PJI was 2.45% in Sweden and 2.17% in Denmark. A model with the underlying diagnosis for THA, body mass index (BMI), American Society for Anesthesiologists (ASA) class, sex, age, and the presence of five defined comorbidities had an area under the curve (AUC) of 0.68 (95% CI: 0.66 to 0.69) in Sweden and 0.66 (95% CI: 0.64 to 0.69) in Denmark. This was superior to traditional models based on ASA class, Charlson, Elixhauser, or the Rx Risk V comorbidity indices. Internal calibration was good for predicted probabilities up to 10%. Conclusion: A new PJI prediction model based on easily accessible data available before THA was developed and externally validated. The model had superior discriminatory ability compared to ASA class alone or more complex comorbidity indices and had good calibration. We provide a web-based calculator (https://erikbulow.shinyapps.io/thamortpred/) to facilitate shared decision making by patients and surgeons.
Purpose: To develop a parsimonious risk prediction model for periprosthetic joint infection (PJI) within 90 days after total hip arthroplasty (THA). Patients and Methods: We used logistic LASSO regression with bootstrap ranking to develop a risk prediction model for PJI within 90 days based on a Swedish cohort of 88,830 patients with elective THA 2008-2015. The model was externally validated on a Danish cohort with 18,854 patients. Results: Incidence of PJI was 2.45% in Sweden and 2.17% in Denmark. A model with the underlying diagnosis for THA, body mass index (BMI), American Society for Anesthesiologists (ASA) class, sex, age, and the presence of five defined comorbidities had an area under the curve (AUC) of 0.68 (95% CI: 0.66 to 0.69) in Sweden and 0.66 (95% CI: 0.64 to 0.69) in Denmark. This was superior to traditional models based on ASA class, Charlson, Elixhauser, or the Rx Risk V comorbidity indices. Internal calibration was good for predicted probabilities up to 10%. Conclusion: A new PJI prediction model based on easily accessible data available before THA was developed and externally validated. The model had superior discriminatory ability compared to ASA class alone or more complex comorbidity indices and had good calibration. We provide a web-based calculator (https://erikbulow.shinyapps.io/thamortpred/) to facilitate shared decision making by patients and surgeons.
Authors: Guy Maoz; Michael Phillips; Joseph Bosco; James Slover; Anna Stachel; Ifeoma Inneh; Richard Iorio Journal: Clin Orthop Relat Res Date: 2015-02 Impact factor: 4.176
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